我是机器学习的新手，正在尝试通过这个教程来入门：
https://www.tensorflow.org/tutorials/estimator/boosted_trees
from __future__ import absolute_import, division, print_function, unicode_literalsimport numpy as npimport pandas as pdfrom IPython.display import clear_outputfrom matplotlib import pyplot as plt# 加载数据集。dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv')y_train = dftrain.pop('survived')y_eval = dfeval.pop('survived')import tensorflow as tftf.random.set_seed(123)fc = tf.feature_columnCATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck', 'embark_town', 'alone']NUMERIC_COLUMNS = ['age', 'fare']def one_hot_cat_column(feature_name, vocab): return tf.feature_column.indicator_column( tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocab))feature_columns = []for feature_name in CATEGORICAL_COLUMNS: # 需要对分类特征进行独热编码。 vocabulary = dftrain[feature_name].unique() feature_columns.append(one_hot_cat_column(feature_name, vocabulary))for feature_name in NUMERIC_COLUMNS: feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))# 使用整个批次，因为数据集非常小。NUM_EXAMPLES = len(y_train)def make_input_fn(X, y, n_epochs=None, shuffle=True): def input_fn(): dataset = tf.data.Dataset.from_tensor_slices((dict(X), y)) if shuffle: dataset = dataset.shuffle(NUM_EXAMPLES) # 对于训练，需要多次循环数据集（n_epochs=None）。 dataset = dataset.repeat(n_epochs) # 内存训练不使用批处理。 dataset = dataset.batch(NUM_EXAMPLES) return dataset return input_fn# 训练和评估输入函数。train_input_fn = make_input_fn(dftrain, y_train)eval_input_fn = make_input_fn(dfeval, y_eval, shuffle=False, n_epochs=1)linear_est = tf.estimator.LinearClassifier(feature_columns)# 训练模型。linear_est.train(train_input_fn, max_steps=100)我删除了大部分不必要的代码，以便使其更加简洁。
一切正常，直到我调用linear_est.train(train_input_fn, max_steps=100)函数。之后，我收到了以下错误消息。请原谅我提供了一个巨大的错误代码块，因为我不知道哪部分是重要的。
2020-01-03 19:10:31.309875: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dllWARNING:tensorflow:Using temporary folder as model directory: C:\Users\bueny\AppData\Local\Temp\tmpqudxt4e5WARNING:tensorflow:From C:\Users\bueny\.conda\envs\TensorFlow\lib\site-packages\tensorflow_core\python\ops\resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.Instructions for updating:If using Keras pass *_constraint arguments to layers.WARNING:tensorflow:From C:\Users\bueny\.conda\envs\TensorFlow\lib\site-packages\tensorflow_core\python\training\training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.Instructions for updating:Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.2020-01-03 19:10:33.796478: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll2020-01-03 19:10:34.518560: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: name: GeForce MX150 major: 6 minor: 1 memoryClockRate(GHz): 1.0375pciBusID: 0000:02:00.02020-01-03 19:10:34.518746: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.2020-01-03 19:10:34.519194: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0WARNING:tensorflow:Layer linear/linear_model is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2. The layer has dtype float32 because it's dtype defaults to floatx.If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.WARNING:tensorflow:From C:\Users\bueny\.conda\envs\TensorFlow\lib\site-packages\tensorflow_core\python\feature_column\feature_column_v2.py:518: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.Instructions for updating:Please use `layer.add_weight` method instead.WARNING:tensorflow:From C:\Users\bueny\.conda\envs\TensorFlow\lib\site-packages\tensorflow_core\python\feature_column\feature_column_v2.py:4276: IndicatorColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.Instructions for updating:The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.WARNING:tensorflow:From C:\Users\bueny\.conda\envs\TensorFlow\lib\site-packages\tensorflow_core\python\feature_column\feature_column_v2.py:4331: VocabularyListCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.Instructions for updating:The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.WARNING:tensorflow:From C:\Users\bueny\.conda\envs\TensorFlow\lib\site-packages\tensorflow_estimator\python\estimator\canned\linear.py:308: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.Instructions for updating:Use `tf.cast` instead.WARNING:tensorflow:From C:\Users\bueny\.conda\envs\TensorFlow\lib\site-packages\tensorflow_core\python\keras\optimizer_v2\ftrl.py:143: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.Instructions for updating:Call initializer instance with the dtype argument instead of passing it to the constructor2020-01-03 19:10:35.759525: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2提前感谢您的帮助，
Benjamin
回答：
我能够按照以下方式重现TF的Boosted_Trees估计器示例的正确结果（未对其代码进行任何修改）：
我猜您可能主要是因为安装了错误版本的TensorFlow（或其他依赖项）而遇到错误。
通过在终端中导入TensorFlow后运行tf.__version__来检查您使用的TensorFlow版本。
希望这对您有帮助！
